import random
from torch import nn
import torch
out_channels = 64
x = torch.rand(16, out_channels*2, 64, 113)

a = nn.Sequential(
            nn.Conv2d(in_channels=out_channels*2, out_channels=out_channels*4, kernel_size=(6, 1), stride=(3, 1), padding=(1, 0)),
            nn.BatchNorm2d(out_channels*4),
            nn.ReLU(True),
            nn.Conv2d(in_channels=out_channels*4, out_channels=out_channels*4, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0)),
            nn.BatchNorm2d(out_channels*4),
            nn.ReLU(True)
        )
b = nn.Sequential(
            nn.ConvTranspose2d(out_channels*4, out_channels*4, kernel_size=(3,1), stride =(1,1), padding=(1,0),output_padding=(0,0)), 
            nn.BatchNorm2d(out_channels*4),
            nn.ReLU(),
            nn.ConvTranspose2d(out_channels*4, out_channels*2, kernel_size=(6,1), stride =(3,1), padding=(1,0),output_padding=(0,0)), 
            nn.BatchNorm2d(out_channels*2),
            nn.ReLU()
        )

x = a(x)
x = b(x)
print()

